*This model was released on 2021-12-02 and added to Hugging Face Transformers on 2023-01-16.*
# Mask2Former
## Overview
The Mask2Former model was proposed in [Masked-attention Mask Transformer for Universal Image Segmentation](https://huggingface.co/papers/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar. Mask2Former is a unified framework for panoptic, instance and semantic segmentation and features significant performance and efficiency improvements over [MaskFormer](maskformer).
The abstract from the paper is the following:
*Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice
of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).*
Mask2Former architecture. Taken from the original paper.
This model was contributed by [Shivalika Singh](https://huggingface.co/shivi) and [Alara Dirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/facebookresearch/Mask2Former).
## Usage tips
- Mask2Former uses the same preprocessing and postprocessing steps as [MaskFormer](maskformer). Use [`Mask2FormerImageProcessor`] or [`AutoImageProcessor`] to prepare images and optional targets for the model.
- To get the final segmentation, depending on the task, you can call [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or [`~Mask2FormerImageProcessor.post_process_instance_segmentation`] or [`~Mask2FormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`Mask2FormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mask2Former.
- Demo notebooks regarding inference + fine-tuning Mask2Former on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Mask2Former).
- Scripts for finetuning [`Mask2Former`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/instance-segmentation).
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
## Mask2FormerConfig
[[autodoc]] Mask2FormerConfig
## MaskFormer specific outputs
[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerModelOutput
[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerForUniversalSegmentationOutput
## Mask2FormerModel
[[autodoc]] Mask2FormerModel
- forward
## Mask2FormerForUniversalSegmentation
[[autodoc]] Mask2FormerForUniversalSegmentation
- forward
## Mask2FormerImageProcessor
[[autodoc]] Mask2FormerImageProcessor
- preprocess
- encode_inputs
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation
## Mask2FormerImageProcessorFast
[[autodoc]] Mask2FormerImageProcessorFast
- preprocess
- post_process_semantic_segmentation
- post_process_instance_segmentation
- post_process_panoptic_segmentation